Perception is integral to intelligence. Perceptual ability is a prerequisite for any intelligent agent, living or artificial, to function satisfactorily in the real world. For an agent to experience an external environment with its perceptual organs (or sensors, in the case of artificial agents), it sometimes becomes necessary to augment the perceptual organs, the environment, or both.
For example, human eyes are often augmented with a pair of prescription glasses. In another example, to experience surround-sound in a car or in a home theater, the environment is augmented with devices, such as speakers and sub-woofers, placed in certain positions with respect to the agent. To experience a 3D movie, the agent often has to wear specially designed eyeglasses, such as polarized glasses. These and other devices including, without limitation, audio headphones, hearing aids, cochlear implants, low-light or “night-vision” goggles, tactile feedback devices, etc., may be referred to generally as “perceptual devices.”
Due to personal preference, taste, and the raw perceptual ability of the organs, the quality of experience achieved by augmenting the agent's perceptual organs or environment with devices is often user-specific. As a result, the devices should be tuned to provide the optimum experience to each user.
With the advent of sophisticated perceptual devices, each having a large number of degrees of freedom, it has become difficult to tune such devices to the satisfaction of each user. Many devices are left to the user for ad-hoc self-tuning, while many others are never tuned because the time and cost required to tune a device for a user may be too high. For example, cochlear implant devices, often used by people having severe hearing-impairment, are virtually never tuned by an audiologist to a particular user, but instead are left with the factory default settings to which the user's brain must attempt to adjust. Thus, a hearing-impaired person may never get the full benefit of his cochlear implant.
Agents with simple perceptual systems (e.g., robotic vacuum cleaners) have sufficient transparency to allow for the tracking of their raw perceptual abilities, while agents with complex perceptual systems (e.g., humans) lack that transparency. Hence, it is extremely difficult to tune devices to the satisfaction of members of the latter class of users, because of the complexity of the devices that enhance an already complex perceptual system.
A sophisticated perceptual device should also allow the user to tune the device to meet that user's particular perceptual needs. Such complex devices often have a large set of parameters that can be tuned to a specific user's needs. Each parameter can be assigned one of many values, and determining the values of parameters for a particular user's optimum performance is difficult. A user is required to be thoroughly tested with the device in order to be assigned the optimum parameter values. The number of tests required increases exponentially with the number of device parameters. Dedicating a significant amount of time to testing often is not a feasible option; accordingly, it is may be advantageous to reduce the complexity of the problem.
Therefore, there is a need to automatically tune perceptual devices in a user-specific way. As of today, living agents, especially humans, have complex perceptual systems that can take advantage of a user-specific tuning method. Artificial agents with complex perceptual systems, when developed, will also benefit from the user-specific tuning method.